Methods and apparatus to expand panelist enrollment

ABSTRACT

Methods, apparatus, systems and articles of manufacture to expand panelist enrollment are disclosed. An example apparatus includes a fingerprint receiver to access a fingerprint received from a media device associated with a panelist, the panelist enrolled in a first panel corresponding to a first category of the media device, the fingerprint identifying devices communicating on a same local area network as the media device. The example apparatus includes a fingerprint analyzer to determine whether a second device within a second category of devices corresponding to a second panel is identified in the fingerprint, the second category of devices not including the media device. The example apparatus includes a panel opportunity identifier to prompt the panelist to join the second panel in response to the determination that the second device within the second category of devices is identified in the fingerprint.

FIELD OF THE DISCLOSURE

This disclosure relates generally to enrolling panelists, and, moreparticularly, to methods and apparatus to expand panelist enrollment.

BACKGROUND

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. That is, anaudience measurement entity enrolls people that consent to beingmonitored into a panel. During enrollment, the audience measuremententity receives demographic information from the enrolling people sothat subsequent correlations may be made between advertisement/mediaexposure to those panelists and different demographic markets. Panelistdevices (e.g., a mobile device, a tablet, etc.) are then instrumentedwith monitoring functionality that provides monitoring information(e.g., metering data) to the audience measurement entity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment of use including anexample system to facilitate expansion of panelist enrollment.

FIG. 2 is a block diagram of an example implementation of the examplefingerprint generator of FIG. 1.

FIG. 3 is an example data table representing panelist demographicinformation collected by the central facility of FIG. 1.

FIG. 4 is an example data table representing panelist enrollment invarious panels.

FIG. 5 is a flowchart representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to generate a fingerprint.

FIG. 5A is an example data table representing network identificationdata that may be collected by the example network scanner of FIG. 2.

FIG. 6 is a flowchart representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to identify nearby wireless networks.

FIG. 7 is a flowchart representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to identify nearby devices.

FIG. 8 is an example data table representing fingerprint data collectedfrom fingerprint generator(s) associated with various panelists.

FIG. 9 is a flowchart representative of example machine readableinstructions which may be executed to implement the example centralfacility of FIG. 1 to identify additional panelist enrollmentopportunities.

FIG. 10 is a flowchart representative of example machine readableinstructions which may be executed to implement the example centralfacility of FIG. 1 to parse fingerprint data.

FIG. 11 is an example data table representing whether the fingerprintgenerator(s) associated with various panelists commonly see devices ofvarious device categories.

FIG. 12 is a block diagram of an example processor platform capable ofexecuting the example machine-readable instructions of FIGS. 5, 6,and/or 7 to implement the example fingerprint generator of FIGS. 1and/or 2.

FIG. 13 is a block diagram of an example processor platform capable ofexecuting the example machine-readable instructions of FIGS. 9 and/or 10to implement the example central facility of FIG. 1.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Monitoring companies desire knowledge on how users interact with mediadevices such as smartphones, tablets, laptops, smart televisions, etc.In particular, media monitoring companies want to monitor mediapresentations made at the media devices to, among other things, monitorexposure to advertisements, determine advertisement effectiveness,determine user behavior, identify purchasing behavior associated withvarious demographics, etc.

As used herein, the term “media” includes any type of content and/oradvertisement delivered via any type of distribution medium. Thus, mediaincludes television programming or advertisements, radio programming oradvertisements, movies, web sites, streaming media, etc.

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. People becomepanelists via, for example, a user interface presented on the mediadevice (e.g., via a website). People become panelists in additional oralternative manners such as, for example, via a telephone interview, bycompleting an online survey, etc. Additionally or alternatively, peoplemay be contacted and/or enlisted using any desired methodology (e.g.,random selection, statistical selection, phone solicitations, Internetadvertisements, surveys, advertisements in shopping malls, productpackaging, etc.).

In some examples, panelists are recruited for participation in aparticular panel that is specific to the type of device that theyfrequently use (e.g., a television, an Apple™ iPhone™, a desktopcomputer, a Google Android™ tablet, etc.). However, as the mediapresentation environment of a panelist may change over time (e.g., asthe panelist acquires new media devices), the enrollment of the panelistin various panels may become outdated. That is, whereas a panelistparticipates in a first panel (e.g., an Apple™ iPhone™ panel), butsubsequently acquires an Android™ tablet, that Android™ tablet might gounmonitored and the media consumption habits of the panelist might notbe completely reflected in collected monitoring data. Enrolling apanelist in as many panels as possible enables a more holisticmeasurement to be made of the media presented to the panelist (e.g., asno or few media devices are omitted from the monitoring effort). Inexamples disclosed herein, when joining and/or registering for anotherpanel, the panelist is not burdened to provide additional demographicinformation (or resubmit information previously provided by the panelist(e.g., provided during registration for another panel)), as suchinformation was collected during a prior panel enrollment (e.g., thepanelist does not provide additional demographic information). However,in some examples, the panelist may be requested by the registrar 155 toconfirm and/or re-submit their demographic information.

Example approaches disclosed herein generate a fingerprint of devicesthat are detected near the primary device of the panelist (e.g., devicesfrequently detected near the primary device). As used herein, afingerprint is a collection of information that can be used to identifyinformation concerning the surroundings of a device. In some examples,the fingerprint can be used to identify a location of the device (e.g.,whether the device has previously been in a location and/or isfrequently in a location). In some examples, fingerprints from multipledifferent devices may be analyzed to determine whether they identify asame location. Example approaches for analyzing a fingerprint and/orinformation collected concerning devices detected near a device aredisclosed in U.S. patent application Ser. No. 14/577,870, which ishereby incorporated by reference in its entirety.

The primary monitored device of the panelist is instrumented with afingerprint generator that monitors, for example, the local area networkto which the device is connected for other commonly seen devices. Insome examples, other information is monitored as well, such as, forexample, names of wireless networks that are commonly near the device,the local IP address of the device, etc. As used herein, devices and/ornetworks are considered to be commonly seen and/or commonly near thedevice when the devices and/or networks are detected a threshold numberof times within a threshold time period. In some examples, otherthresholds and/or criteria may additionally or alternatively be usedsuch as, for example, whether the devices and/or networks were detectedduring a particular day of the week, time of the day, etc. Thefingerprint generator reports such information to a central facility ofthe audience measurement entity. The central facility processes thefingerprints to identify whether the panelist is frequently near devicesfor which additional monitoring might be performed. For example, if afingerprint of a panelist in the iPhone panel indicates that thepanelist's iPhone is frequently near an Android™ tablet, a recruitmentmessage may be presented to the panelist to request the panelist to alsojoin an Android™ panel (e.g., to permit monitoring of the Android™tablet). In some examples, activities other than recruitment messagesmay be performed such as, for example, a survey may be presented to thepanelist.

FIG. 1 is a block diagram of an example environment of use including anexample system to facilitate expansion of panelist enrollment. Theexample environment includes a central facility 110 in communicationwith a fingerprint generator 115 operated at a media device 112 via alocal area network 125 and the Internet 127. A media monitor 130operated at the media device 130 monitors media presented by the mediadevice 112. Because many different types of media devices exist (e.g.,Google Android™ devices, Apple™ devices, desktop computers, streamingmedia devices, etc.), different media monitors may be implemented foroperation at different media monitors (e.g., a first media monitor maybe operated on Google Android™ devices, whereas a second media monitormay be operated on Apple™ devices). Thus, panelists may participate in apanel(s) based on the type(s) of media devices that they own or, morespecifically, the media monitors that their media devices are capable ofoperating.

Media monitoring information collected by the media monitor 130 istransmitted to the central facility 110 via a network communicator 135.The example fingerprint generator 115, via the network communicator 135,scans the local area network 125 to identify other devices 140, 141, 142also connected to the local area network 125. In some examples, theexample fingerprint generator 115 also identifies other wirelessnetworks 145 near the media device 112. Information concerning thedevices 140, 141, 142 connected to the local area network 125 and/or theother wireless networks 145 near the media device 112 is reported to thecentral facility 110. In examples disclosed herein, such collectedinformation is unique to the media device 112 and, as a result, forms afingerprint that might be used to identify information concerning thesurroundings of the media device 112. In some examples, the centralfacility 110 reviews the fingerprint data to identify panelistrecruitment opportunities.

The example central facility 110 of the illustrated example of FIG. 1 isoperated by an audience measurement entity (AME). The example AME is aneutral third party (such as The Nielsen Company (US), LLC) who does notsource, create, and/or distribute media and can, thus, provide unbiasedratings and/or other media monitoring statistics. To create the unbiasedratings and/or other media monitoring statistics, the AME operates thecentral facility 110. The example central facility 110 includes one ormore servers that collect monitoring data from media monitors 130 and/orfingerprint generators 115 associated with media devices 112 and, basedon the collected information, and develops audience measurementstatistics and/or prepares reports. Although only one central facility110 is shown in FIG. 1, many facilities may be provided for collectingthe data. In some examples, these data collection facilities arestructured in a tiered approach with many satellite collectionfacilities collecting data and forwarding the same to one or morecentral facilities 110. In the illustrated example of FIG. 1, theexample central facility 110 includes a network interface 150, aregistrar 155, a panelist database 160, a fingerprint receiver 165, afingerprint database 170, a fingerprint analyzer 175, and a panelopportunity identifier 180.

The example network interface 150 of the illustrated example of FIG. 1receives messages that include monitoring data and/or fingerprint datafrom the media device 112. In examples disclosed herein, the monitoringdata identifies media that was presented by the media device 112. Insome examples, the monitoring data identifies media that was notpresented by the media device 112 itself, but was presented in proximityof the media device 112 (e.g., as monitored through a microphone of themedia device).

In examples disclosed herein, the fingerprint data includesidentifications of devices commonly found on the same local area network125 as the media device 112. In some examples, information concerningother networks near the media device 112 (e.g., names of the nearbynetworks, signal strengths of the nearby networks, etc.) may be includedin the fingerprint data.

In the example of FIG. 1, the registrar 155 receives registrationinformation from a panelist and stores a record identifying the panelistand/or the panelist's media device 112. In the illustrated example, thereceived registration information includes demographic information.However, any other information may additionally or alternatively becollected. The registration information may include, for example,information identifying the model of the media device 112 associatedwith the panelist, a mailing address associated with the panelist, anemail address associated with the panelist, a phone number associatedwith the media device 112, a unique identifier of the panelist and/ormedia device 112 (e.g., a social security number of the panelist, aphone number of the media device 112, a zip code of the panelist, and/orany combination or derivation of any information related to the panelistand/or the media device 112), the age of the panelist, the gender of thepanelist, the race of the panelist, the marital status of the panelist,the income of the panelist and/or the household of the panelist, theemployment status of the panelist, where the panelist typically intendto use the media device 112, how long the panelist has owned the mediadevice 112, the education level of the panelist, and/or any otherinformation related to the panelist and/or the media device 112. Theexample registrar 155 stores the received demographic information in thepanelist database 160.

The example panelist database 160 of the illustrated example of FIG. 1may be implemented by any device for storing data such as, for example,flash memory, magnetic media, optical media, etc. Furthermore, the datastored in the example panelist database 160 may be in any format suchas, for example, binary data, comma separated data, tab delimited data,structured query language (SQL) structures, etc. While, in theillustrated example, the example panelist database 160 is illustrated asa single database, the example panelist database 160 may be implementedby any number and/or type(s) of database(s). The example panelistdatabase 160 stores panelist demographic information received by theregistrar 155. An example data table reflecting panelist registrationdata is described below in connection with FIG. 3. The example panelistdatabase 160 stores monitoring data received from the media monitor 130of the media device 112. In examples disclosed herein, the examplepanelist database 160 stores information concerning the panels in whichthe panelist is enrolled. An example data table reflectingidentifications of the panels in which a panelist is enrolled isdescribed below in connection with FIG. 4.

The example fingerprint receiver 165 of the illustrated example of FIG.1 receives a fingerprint from the fingerprint generator 115, and storesthe received fingerprint in the fingerprint database 170. In examplesdisclosed herein, the fingerprints are received via hypertext transferprotocol (HTTP) messages. However, any other past, present, and/orfuture messaging format and/or protocol may additionally oralternatively be used. In examples disclosed herein, the fingerprintreceiver 165 updates the fingerprint stored in the fingerprint database170 in connection with the panelist and/or fingerprint generator 115from which the fingerprint is received. That is, previous versions ofthe fingerprint are overwritten. However, in some examples, the priorversions of the fingerprint(s) are retained such that they can beanalyzed at a later time, if necessary. While in the illustrated exampleof FIG. 1 the example fingerprint receiver 165 receives a fingerprintfrom the fingerprint generator 115 (e.g., a fingerprint that has beengenerated by the fingerprint generator 115), in some examples, thefingerprint receiver 165 may receive information that would otherwise beused to create the fingerprint such that the fingerprint generationoccurs at the central facility 110, rather than at the media device 112.

The example fingerprint database 170 of the illustrated example of FIG.1 may be implemented by any device for storing data such as, forexample, flash memory, magnetic media, optical media, etc. Furthermore,the data stored in the example fingerprint database 170 may be in anyformat such as, for example, binary data, comma separated data, tabdelimited data, structured query language (SQL) structures, etc. While,in the illustrated example, the example fingerprint database 170 isillustrated as a single database, the example fingerprint database 170may be implemented by any number and/or type(s) of database(s). Theexample fingerprint database 170 stores fingerprint information receivedfrom the fingerprint generators 115 of respective media devices 112.

The example fingerprint analyzer 175 of the illustrated example of FIG.1 analyzes fingerprints stored in the fingerprint database 170 todetermine whether a device of a particular category is commonlyconnected to the same local area network as the media device 112. Inexamples disclosed herein, pattern matching based on one or morepatterns of device names (and/or other identifiers) is used to determinewhether a device of a particular category is commonly connected to thesame local area network as the media device 112. For example, theexample fingerprint analyzer 175 may evaluate whether a device namematching the pattern “APPLE IPAD” to indicate a device is present in thecategory “APPLE DEVICE”. In some examples, regular expressions and/orother pattern matching techniques may be used by the example fingerprintanalyzer 175.

The example panel opportunity identifier 180 of the illustrated exampleof FIG. 1 identifies opportunities to present messages to panelists.Such messages may be used, for example, to request that the panelistenroll in another panel, to present a survey to the panelist, to confirmdemographic information of the panelist, to confirm device ownership ofthe panelist, etc. In examples disclosed herein, the example panelopportunity identifier 180 compares fingerprint data parsed by thefingerprint analyzer 175 against panel opportunity data (e.g., a requestfor data provided by an administrator or other personnel of the audiencemeasurement entity) and/or panelist information stored in the panelistdatabase 160 to identify opportunities. In some examples, the examplepanel opportunity identifier 180 interacts with the fingerprintgenerator 115 (and/or another component of the media device 112) topresent the message to the panelist.

The example media device 112 of the illustrated example of FIG. 1 is adevice that is capable of presenting media. The example media device 112may be, for example, a tablet, a laptop computer, a smart phone, etc. Inthe illustrated example, the media monitor 130 monitors mediapresentations of the media device 112, and reports monitoring dataconcerning such media presentations to the central facility 110. In someexamples, the monitoring data is reported based on monitoringinstructions implementing the media monitor 130 received from theaudience measurement entity. For example, the media monitor 130 isimplemented by an app and/or instructions downloaded to the media device112 from the central facility 110 and/or an app store. However, anyother approach to providing and/or structuring monitoring instructions,and/or reporting monitoring data to a central facility 110 mayadditionally or alternatively be employed. For example, the mediamonitor 130 may be structured in accordance with the teachings ofBlumenau, U.S. Pat. No. 6,108,637.

In the illustrated example of FIG. 1, the media device 112 is associatedwith a single user. However, in some examples, the media device 112 maybe associated with multiple users. In the illustrated example, the mediadevice 112 communicates via the local area network 125. The examplemedia device 112 communicates via any number of different local areanetworks during use of the media device (e.g., when the media device 112is moved to a different physical location).

The example network communicator 135 of the illustrated example of FIG.1 is a wireless radio that communicates with an access point of thelocal area network 125. In the illustrated example, the networkcommunicator 135 includes a WiFi radio for communicating with the localarea network 125 (and/or an access point of the local area network 125).However, any other number and/or type(s) of radios may additionally oralternatively be used such as, for example, a cellular radio. Theexample network communicator 135 may communicate using any past,present, and/or future communication protocols such as, for example,WiFi, Global System for Mobile Communications (GSM), code divisionmultiple access (CDMA), long term evolution (LTE), etc.

In the illustrated example, the monitoring information (e.g., monitoringinformation collected by the media monitor 130 and/or the fingerprintgenerated by the fingerprint generator 115) is transmitted to thecentral facility 110 using an HTTP message. However, any other approachto transmitting data may additionally or alternatively be used such as,for example, a file transfer protocol (FTP), HTTP Secure (HTTPS), anHTTP Get request, Asynchronous JavaScript and extensible markup language(XML) (AJAX), etc.

The example fingerprint generator 115 of the illustrated example of FIG.1 scans the local area network 125 to identify the other devices 140,141, 142 also connected to the local area network 125. An exampleimplementation of the fingerprint generator 115 is disclosed below inconnection with FIG. 2. In some examples, the example fingerprintgenerator 115 also identifies other wireless networks 145 near the mediadevice 112. Information concerning the devices 140, 141, 142 connectedto the local area network 125 and/or the other wireless networks 145near the media device 112 is reported to the central facility 110. Inexamples disclosed herein, such collected information is unique to themedia device 112 and, as a result, forms a fingerprint that might beused to identify information concerning the surroundings of the mediadevice 112. The example fingerprint generator 115 maintains a list ofrecently seen devices and/or networks, and uses such information tomaintain a fingerprint representative of commonly seen devices and/ornetworks. The fingerprint is reported to the central facility 110.

Such fingerprint information is useful in that, if a panelist wereenrolled in a particular panel (e.g., an iOS panel), but it wasdiscovered an Android™ tablet was frequently seen on the same local areanetwork as the media device, that panelist (or a family member of thepanelist) may be more likely to join the Android™ panel. A solicitationto join the Android™ panel may then be presented to the panelist.Prompting existing panelists to join additional panels is advantageousbecause registration information for such panelist is already known (andmay not need to be collected again). In some examples, other propertiesof the panelist might be considered such as, for example, how long thepanelist has been a panelist, whether the panelist is compliant withoperation of the media device, whether the panelist is responsive torequests from the audience measurement entity.

In some examples, the fingerprint information concerning the recentlyseen devices and/or networks might also be used to present a survey tothe panelist. For example, if the media device 112 frequently sees aparticular type of printer on the same local area network as the mediadevice 112, a survey concerning the printer might be presented to thepanelist. Additionally or alternatively, if the media device 112 isfrequently near a wireless network commonly associated with a commercialestablishment (e.g., a coffee shop, a restaurant, an airport), a surveymay be presented concerning the commercial establishment.

The example local area network 125 of the illustrated example of FIG. 1is a wireless LAN within a home of a panelist. However, in someexamples, the example local area network 125 is at a location other thana home such as, for example, a library, a retail establishment, a publiclocation, etc. In the illustrated example, the example local areanetwork 125 includes an access point 126 (e.g., a router, a gateway,etc.) that is in communication with the Internet 127 via a serviceprovider. In some examples, the access point 126 is a router thatenables multiple devices in communication with the local area network125 to communicate via the Internet 127. In the illustrated example, theaccess point 126 hosts the wireless local area network (LAN) using, forexample, WiFi. However, any other past, present, and/or future approachto hosting a local area network may additionally or alternatively beused. The access point 126 issues a private IP address to each devicethat is communicatively coupled to the example local area network 125.The private IP address enables devices on the LAN (e.g., the mediadevice 112, the devices 140, 141, 142) to communicate with the accesspoint 126.

As mentioned above, within the LAN 125 hosted by the example accesspoint 126, individual devices are each given a private IP address. Inthe illustrated example, the private IP addresses are assigned using adynamic host of protocol (DHCP). When a device within the LAN 125transmits a request to a resource outside the LAN 125 (e.g., on theInternet 127), the example access point 126 translates the originatingprivate IP address of the device making the request to the publicaddress of the example access point 126 before relaying the requestoutside the LAN 125 (e.g. to the destination). Thus, when the resourceoutside the LAN 125 receives the request, the resource is able totransmit a return response to the LAN 125 (and/or the access point 126).On the return path, the example access point 126 translates thedestination IP address of the response to the private IP address of therequesting device so that the return message may be delivered to thedevice that made the original request.

As the panelist likely will carry the media device 112 throughout theday, the media device 112 is likely to encounter other networks 145.Such other networks 145 are, for example, wireless networks associatedwith various businesses, homes, etc. In examples disclosed herein, theother networks 145 are WiFi networks. However, any other networkingtechnology may additionally or alternatively be used such as, forexample, a cellular network. The example fingerprint generator 115records information indicative of the presence of the other network 145as part of the fingerprint data.

FIG. 2 is a block diagram of an example implementation of the examplefingerprint generator of FIG. 1. The example fingerprint generator 115of FIG. 2 includes a network scanner 210, a network informationprocessor 220, a temporary network information data store 215, afingerprint data store 240, a fingerprint reporter 250, and arecruitment controller 260. In operation, the network scanner 210 scansthe local area network 125 to collects information concerning the otherdevices present on the local area network 125. The collected informationis stored in the temporary network information data store 215. Thenetwork information processor 220 processes the information concerningthe other devices present on the local area network 125 stored in thetemporary network information data store 215 to create a fingerprintrepresenting devices that are commonly seen by the media device 112. Theexample network information processor 220 stores the fingerprint in thefingerprint data store 240. The fingerprint reporter 250 reports thefingerprint stored in the fingerprint data store 240 to the centralfacility 110. Upon an indication from the central facility, therecruitment controller 260 presents an interface to the panelist.

The example network scanner 210 of the illustrated example of FIG. 2scans the local area network 125 to which the media device 112 isconnected to identify other devices on the local area network 125. Inexamples disclosed herein, the example network scanner 210 determines apossible range of IP addresses in use on the local area network, andtransmits a message (e.g., an Internet Control Message Protocol (ICMP)echo request) to each potential IP address. If a response is receivedfrom the tested IP address, further investigation is performed to gatherinformation concerning the device that responded at the tested IPaddress. For example, the IP address, the network name, a hardwareidentifier (e.g., a media access control (MAC) address) of the device,etc. may be identified. In some examples, a request (e.g., an HTTPrequest) may be transmitted to the device responding at the tested IPaddress and a response to the request may be analyzed to identifyinformation concerning the device.

In some examples, the network scanner 210 interfaces with the networkcommunicator 135 to identify a nearby wireless local area network. Asused herein a wireless network is nearby when it is within wirelesssignal communication range. Wireless signal communication ranges mightvary based on the location and/or wireless communication technologyused. For example, a WiFi network typically has a wireless communicationrange of a three hundred feet. In some examples, the wirelesscommunication range of a WiFi network is reduced by environmentalimpediments (e.g., walls, doors, trees, etc.). However, any otherwireless communication technology having any other wirelesscommunication range (e.g., a cellular communication having a range of amile.) The example media device 112 does not need to be connected to thenearby wireless local area network. In some examples, the identificationof the wireless local area network identifies a name (e.g., a ServiceSet Identifier (SSID)) of the wireless local area network, and a signalstrength of the wireless local area network.

In some examples, a wireless network may be within the wireless signalcommunication range, but may have a very low signal strength. In someexamples, when the signal strength of the wireless network is low, thewireless network is not considered nearby (even though the network istechnically within the wireless signal communication range). Thus, insome examples, the example network scanner 210 determines whether thesignal strength is greater than a threshold value when determiningwhether to consider the wireless local area network as nearby. Lowsignal strength is typically an indicator that the wireless network iswithin wireless communication range, but not very close to the mediadevice 112. In some examples, the network scanner 210 identifies thenetwork to which the media device is currently connected.

The example network scanner 210 stores identifications of devicesconnected to the same local area network as the media device 112 and/oridentifications of nearby wireless local area networks in the exampletemporary network information data store 215. An example tablerepresenting data stored by the example network scanner 210 in theexample temporary network information data store 215 is described belowin connection with FIG. 5A.

The example temporary network information data store 215 of theillustrated example of FIG. 2 may be implemented by any device forstoring data such as, for example, flash memory, magnetic media, opticalmedia, etc. Furthermore, the data stored in the example temporarynetwork information data store 215 may be in any format such as, forexample, binary data, comma separated data, tab delimited data,structured query language (SQL) structures, etc. While, in theillustrated example, the example temporary network information datastore 215 is illustrated as a single database, the example temporarynetwork information data store 215 may be implemented by any numberand/or type(s) of database(s). The example temporary network informationdata store 215 stores information concerning devices recentlyencountered on the same local area network as the media device 112and/or wireless local area networks that have been nearby the mediadevice 112. An example data table reflecting such information isdescribed below in connection with FIG. 5A.

The example network information processor 220 of the illustrated exampleof FIG. 2 reviews entries in the temporary network information datastore 215 to identify wireless networks that are commonly nearby themedia device 112 and adds those identified wireless networks to thefingerprint stored in the example fingerprint data store 240. Likewise,the example network information processor 220 reviews entries in thetemporary network information data store 215 to identify devices thatare commonly connected to the same local area network as the mediadevice 112 and adds those identified devices to the fingerprint storedin the example fingerprint data store 240.

In some examples, the example network information processor 220 adds awireless network and/or a device identifier to the fingerprint when thenetwork and/or device has been seen more than a first threshold numberof times (e.g., ten times) within a first threshold period of time(e.g., the past week). However, any other thresholds and/or approachesto approach to determining when a wireless network and/or a device iscommonly seen may additionally or alternatively be used. In someexamples, the example network information processor 215 reviews devicesidentified in the fingerprint (e.g., stored in the fingerprint datastore 240) to identify and remove wireless networks and/or devices thatare no longer commonly seen from the fingerprint. For each wirelessnetwork and/or device identified in the fingerprint, the example networkinformation processor 215 determines whether the wireless network and/ordevice has been identified a second threshold number of times (e.g.,five times) within a second threshold period of time (e.g., the pastmonth). As compared to the first threshold number of times and the firstthreshold period of time, the second threshold number of times andsecond threshold period of time are less restrictive. That is, awireless network and/or device that was previously seen frequently suchthat it was added to the fingerprint, but is now seen occasionally, willremain a part of the fingerprint until it has not been seen the secondthreshold number of times within the second threshold period of time.However, any other thresholds may additionally or alternatively be used.Wireless networks and/or devices that do not meet the second thresholdnumber of times within the second threshold period of time are removedfrom the fingerprint (but may later be re-added if they again becomefrequently seen). As with adding items to the fingerprint, differentthresholds may be used when considering whether to remove a wirelessnetwork or a device from the fingerprint.

In addition to storing a wireless network identifier (e.g., a wirelessnetwork name) and/or a device identifier (e.g., a device name), anyother information concerning the wireless network and/or device mayadditionally or alternatively be stored as part of the fingerprint. Forexample, the fingerprint may reflect a local IP address of the mediadevice 112, a cellular signal strength reported by a cellular radio ofthe media device 112, a make and/or model of the other devices 140, 141,142, a form factor of the other device 140, 141, 142, an age of theother device 140, 141, 142, a feature and/or functionality provided bythe other device 140, 141, 142, etc.

The example fingerprint data store 240 of the illustrated example ofFIG. 2 may be implemented by any device for storing data such as, forexample, flash memory, magnetic media, optical media, etc. Furthermore,the data stored in the example fingerprint data store 240 may be in anyformat such as, for example, binary data, comma separated data, tabdelimited data, structured query language (SQL) structures, etc. While,in the illustrated example, the example fingerprint data store 240 isillustrated as a single database, the example fingerprint data store 240may be implemented by any number and/or type(s) of database(s). Theexample fingerprint data store 240 stores the fingerprint generated bythe network information processor 220.

The example fingerprint reporter 250 of the illustrated example of FIG.2 reports the fingerprint stored in the fingerprint data store 240 tothe fingerprint receiver 165 of the central facility 110. In examplesdisclosed herein, the fingerprint reporter 250 reports the fingerprintperiodically. However, any other period and/or aperiodic approach toreporting the fingerprint may additionally or alternatively be used. Forexample, the example fingerprint reporter 250 may report the fingerprintupon detection of a change in the fingerprint stored in the fingerprintdata store 240. In examples disclosed herein, the fingerprint isreported using an HTTP message. However, any other approach totransmitting data may additionally or alternatively be used such as, forexample, a file transfer protocol (FTP), HTTP Secure (HTTPS), an HTTPGet request, Asynchronous JavaScript and extensible markup language(XML) (AJAX), etc.

The example recruitment controller 260 of the illustrated example ofFIG. 2 controls whether a recruitment interface (e.g., a solicitation tojoin a panel, a survey, etc.) is displayed to the user. In someexamples, the recruitment controller 260 displays a demographicinformation collection interface that enables the panelist to verifytheir demographic information upon enrollment into a panel (e.g., theinitial panel for a panelist, a subsequent/additional panel, etc.) Whendisplaying the demographic information collection interface, the examplerecruitment controller 260 collects demographic information from theuser and transmits the collected information to the central facility110.

FIG. 3 is an example data table representing panelist demographicinformation collected by the central facility of FIG. 1. The exampletable 300 includes a first column 310 storing a user identifier (e.g.,an identifier of the panelist issued by the registrar 155), a secondcolumn 320 storing first demographic information (e.g., an age), and athird column 330 storing second demographic information (e.g., anincome). While in the illustrated example of FIG. 3 there are twocolumns storing demographic information, any other number of columnsstoring any other information may additionally or alternatively be used.For example, additional columns storing other demographic information(e.g., ethnicity, mailing address, sex, etc.) may be used. Additionallyor alternatively, columns storing panelist information (e.g., a panelistaccount creation date, a “last accessed” date, user preferences, etc.)may be used.

In the illustrated example, four panelist records are shown. A firstrecord 340 represents panelist “A”. A second record 350 representspanelist “B”. A third record 1087 represents panelist “C”. A fourthrecord 370 represents panelist “D”. While in the illustrated example ofFIG. 3, four panelist records are shown, any other number of records mayadditionally or alternatively be used. For example, the panelistdatabase 160 may store fifty thousand records respectively associatedwith fifth thousand panelists. In examples disclosed herein, panelistidentifiers are simplified for ease of understanding. However, inpractice, any identifier (e.g., a serial number, an MD5 hash of a name,etc.) may additionally or alternatively be used to identify a panelist.

FIG. 4 is an example data table 400 representing panelist enrollments invarious panels. The example table 400 includes a first column 410storing a user identifier (e.g., an identifier of the panelist issued bythe registrar 155), a second column 420 indicating whether the panelistis enrolled in an iOS panel, a third column 430 indicating whether thepanelist is enrolled in a desktop panel 430, and a fourth column 440indicating whether the panelist is enrolled in an Android panel 440. Inthe illustrated example of FIG. 4, the second column 420, the thirdcolumn 430, and the fourth column 440 indicate active and/or currentpanel enrollments. However, in some examples, past panel enrollment mayadditionally be reflected by the second column 420, the third column430, and the fourth column 440 (e.g., whether a panelist was previouslypart of a panel but has since left the panel).

In the illustrated example of FIG. 4, four panelist records are shown,which correspond to the four panelist records of FIG. 3. A first record450 indicates that panelist “A” is enrolled in the iOS panel, but notthe desktop or Android panels. A second record 460 indicates thatpanelist “B” is enrolled in the desktop panel and the Android panel, butnot the iOS panel. A third record 470 indicates that panelist “C” isenrolled in the Android panel, but not the iOS panel or the desktoppanel. A fourth record 480 indicates that panelist “D” is enrolled inthe iOS panel, but not the desktop or Android panels.

While an example manner of implementing the example central facility 110is illustrated in FIG. 1 and an example manner of implementing theexample fingerprint generator 115 of FIG. 1 is illustrated in FIG. 2,one or more of the elements, processes and/or devices illustrated inFIGS. 1 and/or 2 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the examplenetwork interface 150, the example registrar 155, the example panelistdatabase 160, the example fingerprint receiver 165, the examplefingerprint database 170, the example fingerprint analyzer 175, theexample panel opportunity identifier 180, and/or, more generally, theexample central facility 110 of FIG. 1, the example network scanner 210,the example temporary network information data store 215, the examplenetwork information processor 220, the example fingerprint data store240, the example fingerprint reporter 250, the example recruitmentcontroller 250, and/or, more generally, the example fingerprintgenerator 115 of FIGS. 1 and/or 2 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example network interface 150,the example registrar 155, the example panelist database 160, theexample fingerprint receiver 165, the example fingerprint database 170,the example fingerprint analyzer 175, the example panel opportunityidentifier 180, and/or, more generally, the example central facility 110of FIG. 1, the example network scanner 210, the example temporarynetwork information data store 215, the example network informationprocessor 220, the example fingerprint data store 240, the examplefingerprint reporter 250, the example recruitment controller 250,and/or, more generally, the example fingerprint generator 115 of FIGS. 1and/or 2 could be implemented by one or more analog or digitalcircuit(s), logic circuits, programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). Whenreading any of the apparatus or system claims of this patent to cover apurely software and/or firmware implementation, at least one of theexample network interface 150, the example registrar 155, the examplepanelist database 160, the example fingerprint receiver 165, the examplefingerprint database 170, the example fingerprint analyzer 175, theexample panel opportunity identifier 180, and/or, more generally, theexample central facility 110 of FIG. 1, the example network scanner 210,the example temporary network information data store 215, the examplenetwork information processor 220, the example fingerprint data store240, the example fingerprint reporter 250, the example recruitmentcontroller 250, and/or, more generally, the example fingerprintgenerator 115 of FIGS. 1 and/or 2 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example central facility 110 of FIG. 1 and/or the examplefingerprint generator 115 of FIGS. 1 and/or 2 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and/or 2, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example fingerprint generator 115 of FIGS. 1 and/or 2are shown in FIGS. 5, 6, and/or 7. In these examples, the machinereadable instructions comprise a program for execution by a processorsuch as the processor 1212 shown in the example processor platform 1200discussed below in connection with FIG. 12. The program may be embodiedin software stored on a tangible computer readable storage medium suchas a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), a Blu-ray disk, or a memory associated with the processor 1212,but the entire program and/or parts thereof could alternatively beexecuted by a device other than the processor 1212 and/or embodied infirmware or dedicated hardware. Further, although the example program isdescribed with reference to the flowcharts illustrated in FIGS. 5, 6,and/or 7, many other methods of implementing the example fingerprintgenerator 115 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

Flowcharts representative of example machine readable instructions forimplementing the example central facility 110 of FIG. 1 are shown inFIGS. 9 and/or 10. In these examples, the machine readable instructionscomprise a program for execution by a processor such as the processor1312 shown in the example processor platform 1300 discussed below inconnection with FIG. 13. The program may be embodied in software storedon a tangible computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 1312, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 1312 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowcharts illustrated in FIGS. 9 and/or 10, many othermethods of implementing the example central facility 110 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined.

As mentioned above, the example processes of FIGS. 5, 6, 7, 9 and/or 10may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a tangible computer readablestorage medium such as a hard disk drive, a flash memory, a read-onlymemory (ROM), a compact disk (CD), a digital versatile disk (DVD), acache, a random-access memory (RAM) and/or any other storage device orstorage disk in which information is stored for any duration (e.g., forextended time periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 5, 6, 7, 9, and/or 10may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, when the phrase “at least” is used as the transition termin a preamble of a claim, it is open-ended in the same manner as theterm “comprising” is open ended.

FIG. 5 is a flowchart 500 representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to generate a fingerprint. In theillustrated example of FIG. 5, the example fingerprint generator 115 ofFIGS. 1 and/or 2 collects information concerning nearby wirelessnetworks and/or devices connected to the local area network, and createsand/or updates a fingerprint for the media device 112. The exampleprocess 500 of FIG. 5 begins when the network information processor 220determines that the fingerprint is to be updated (and/or created) (block510). In the illustrated example of FIG. 5, the fingerprint is updatedperiodically. As such, the example network information processor 220determines whether a threshold time has been reached by consulting acounter or other time tracking device. However, any other periodicand/or aperiodic approach to determining whether the fingerprint shouldbe updated may additionally or alternatively be used. In examplesdisclosed herein, the example network information processor 220 mayadditionally or alternatively determine that the fingerprint is to beupdated based on a schedule (e.g., updates are to be performed at one ormore times per day, one or more days per week, etc.). Using a schedulein addition to or as an alternative to a periodic fingerprint updatingapproach ensures that additional devices belonging to other familymembers (which tend to be discovered in the evening and throughout thenight when the whole family is at home or over the weekend) areidentified. If, for example, updating were performed primarily in themornings and rarely in the evenings and/or overnight, devices associatedwith family members might not be identified as part of the fingerprintand/or may be removed from the fingerprint.

If the fingerprint is to be updated (block 510 returns a result of YES),the example network scanner 210 identifies nearby wireless networks(block 520). Identified wireless networks are stored in the temporarynetwork information data store 215. An example data table 591, shown inFIG. 5A, representative of the example data stored in the temporarynetwork information data store 215 is described below. An exampleapproach to identifying nearby wireless networks is disclosed inconnection with FIG. 6.

FIG. 6 is a flowchart representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to identify nearby wireless networks. Theexample process 520 of the illustrated example of FIG. 6 begins when theexample network scanner 210 determines that a scan is to be performed(block 520 of FIG. 5). In the illustrated example, the example networkscanner 210 interfaces with the network communicator 135 to identify anearby wireless local area network (block 610). In the illustratedexample of FIG. 6, the identification of the wireless local area networkidentifies a name (e.g., a Service Set Identifier (SSID)) of thewireless local area network, and a signal strength of the wireless localarea network. The example network scanner 210 determines whether thesignal strength is greater than a threshold value (block 620). Inexamples disclosed herein, the signal strength threshold is −60 decibels(dB). However, any other threshold value may additionally oralternatively be used. If the signal strength is greater than thethreshold (block 620 returns a result of YES), the example networkscanner adds a record of the name of the network (and/or the signalstrength) to the temporary network information data store 215 (block630). As a result, wireless networks that do not meet the thresholdsignal strength are not stored in the temporary network information datastore 215. The example determines whether there are nearby networks(block 640). If there are additional nearby networks (block 640 returnsa result of YES), the process of FIG. 6 is repeated until no additionalnearby wireless local area networks exist for identification. Once allnearby networks have been identified (block 640 returns a result of NO),the process of FIG. 6 terminates.

Returning to FIG. 5, the example network scanner 210 determines whetherthe media device 112 is connected to a local area network 125 (block530). In some examples, the example network scanner 210 determineswhether the media device 112 is connected to the local area network 125by determining whether the media device 112 has been issued an IPaddress on the local area network 125. If the media device 112 isconnected to the local area network (block 530 returns a result of YES),the example network scanner identifies devices connected to the localarea network (block 540). Identifications of identified devices arestored in the temporary network information data store 215. The exampledata table 591 of FIG. 5A represents the example data stored in thetemporary network information data store 215, and is described below. Anexample approach to identifying devices connected to the local areanetwork is disclosed in FIG. 7.

FIG. 7 is a flowchart representative of example machine readableinstructions which may be executed to implement the example fingerprintgenerator of FIGS. 1 and/or 2 to identify nearby devices. The exampleprocess 540 of the illustrated example of FIG. 7 begins when the examplenetwork scanner 210 determines that a scan is to be performed (block 530of FIG. 5). In the illustrated example, the example network scanner 210identifies potential local area network IP addresses that may be used byother devices (block 710). In the illustrated example, the examplenetwork scanner 210 identifies potential local area network IP addressesbased on a subnet that the private IP address of the media device 112 ispart of. Identifying potential local area network IP addresses based onthe subnet of which the fixed device is a part of reduces the overallsearch space of potential addresses (e.g., any potential private IPaddress) to potential IP addresses that are in use on the local areanetwork 125. The example network scanner 210 transmits a message (e.g.,an ICMP echo request) to each potential IP address from the reducedsearch space. (Block 720). The example network scanner 210 awaits aresponse to the message. (Block 740).

If a response is received, the example network scanner 210 interrogatesthe device to gather identifying information concerning the device thatresponded at the tested IP address. (Block 750). In some examples, ahardware identifier of the device is gathered. In the illustratedexample, the hardware identifier is the media access control (MAC)address and can be used to determine a make and/or a model of the otherdevice. However, any other approach to gathering identifying informationconcerning the device that responded at the tested IP address mayadditionally or alternatively be used such as, for example, an HTTPrequest, a NetBIOS request, etc. In some examples, the identifyinginformation is the IP address of the device that responded at the testedIP address. In such an example, further interrogation of the device togather identifying information might not be necessary (as the IP addressis known based on the test that was performed in connection with block720). In some examples, the IP addresses in use on the network (and/orother identifying information) form a fingerprint for the network,thereby enabling an identification of whether the device is at apreviously known (e.g., previously fingerprinted) location. Additionallyor alternatively, the fingerprint for the network might be used todetermine when a first device is at a same location as a second device(e.g., when the fingerprints match).

The example network scanner 210 adds the information identifying thedevice to the temporary network information data store 215. (Block 760).In examples disclosed herein, a timestamp representing the time at whichthe device was identified is also stored. Storing the timestamp enablesa determination of whether the device frequently and/or recentlyconnected to the same LAN 125 as the media device 112.

The example network scanner 210 waits a threshold amount of time todetermine if a response has been received from the tested IP address. Inthe illustrated example, the example network scanner 210 waits for onesecond, as most devices on a local area network will return a responsewithin a matter of milliseconds. However, any other threshold amount oftime may additionally or alternatively be used. If no response wasreceived from a tested IP address within the threshold amount of time(Block 740 returns a result of NO), or once the hardware address hasbeen reported (Block 760), the example network scanner 210 determineswhether there are additional IP addresses to scan. (Block 770). If thereare additional IP addresses to scan, control returns to block 720, wherethe additional IP addresses are scanned in the manner described above.The example network scanner 210 continues to perform the network scanuntil all potential local-area network IP addresses have been tested.Once all IP addresses have been tested (e.g., block 770 returns a resultof NO), the example process of FIG. 7 terminates.

Returning to FIG. 5, upon completion of the identification of devicesconnected to the local area network (block 540) or the determinationthat the media device 112 is not connected to the local area network 125(block 530 returning a result of NO), the example network informationprocessor 220 reviews entries in the temporary network information datastore 215. As noted above, the example data table 591 of FIG. 5Arepresents example data stored in the temporary network information datastore 215. The example data table 591 of FIG. 5A includes a timestampcolumn 589 and an identified item column 592. However, any otherinformation may additionally or alternatively be stored such as, forexample, signal strengths of identified wireless networks, additionalidentifiers of identified devices, etc. The example data table 591 ofFIG. 5A includes a first record 593, a second record 594, a third record595, a fourth record 596, a fifth record 597, a sixth record 598, and aseventh record 599. In practice, many additional records will likely berecorded representative of many other identified devices and/or wirelessnetworks identified by the network scanner 210. In the illustratedexample of FIG. 5A, the first record 593 identifies a first time whenthe media device 112 was near a “STARBUCKS” wireless network. The secondrecord 594, the third record 595, and the fourth record 596 identify asecond time when the media device 112 was connected to a home networkthat also had a Microsoft™ Surface Pro™ computer and an Apple iPad™connected. The fifth record 597, the sixth record 598, and the seventhrecord 599 identify a third time when the media device was connected tothe home network, which also had the Microsoft Surface Pro computer, andan HP desktop computer connected.

The example network information processor 220 reviews entries in thetemporary network information data store 215 to identify wirelessnetworks that are commonly nearby the media device 112 and adds thoseidentified wireless networks to the fingerprint (block 550) stored inthe example fingerprint data store 240. In examples disclosed herein,when a wireless network is seen more than a first threshold number oftimes (e.g., ten times) within a first threshold period of time (e.g.,the past week), the wireless network is considered to be commonly nearbythe media device 112. However, any other thresholds and/or approaches toapproach to determining when a wireless network is commonly seen mayadditionally or alternatively be used.

Likewise, the example network information processor 220 reviews entriesin the temporary network information data store 215 to identify devicesthat are commonly connected to the same local area network as the mediadevice 112 and adds those identified devices to the fingerprint (block560) stored in the example fingerprint data store 240. In examplesdisclosed herein, when a device is seen more than the first thresholdnumber of times (e.g., ten times) within the first threshold period oftime (e.g., the past week), the device is considered to be commonlyconnected to the same local area network as the media device 112.However, any other thresholds and/or approaches to approach todetermining when a wireless network is commonly seen may additionally oralternatively be used. Moreover, different thresholds may be used whenidentifying commonly seen devices as compared to commonly seen wirelessnetworks.

The example network information processor 215 reviews wireless networksidentified in the fingerprint (e.g., stored in the fingerprint datastore 240) to identify and remove wireless networks that are no longercommonly seen from the fingerprint (block 570). For each wirelessnetwork identified in the fingerprint, the example network informationprocessor 215 determines whether the wireless network has beenidentified a second threshold number of times (e.g., five times) withina second threshold period of time (e.g., the past month). In theillustrated example of FIG. 5, as compared to the first threshold numberof times and the first threshold period of time, the second thresholdnumber of times and second threshold period of time are lessrestrictive. That is, a network that was previously seen frequently suchthat it was added to the fingerprint, but is now seen occasionally, willremain a part of the fingerprint until it has not been seen the secondthreshold number of times within the second threshold period of time.However, any other thresholds may additionally or alternatively be used.Wireless networks that do not meet the second threshold number of timeswithin the second threshold period of time are removed from thefingerprint (but may later be re-added if they again become frequentlyseen).

The example network information processor 215 reviews devices identifiedin the fingerprint (e.g., stored in the fingerprint data store 240) toidentify and remove devices that are no longer commonly seen from thefingerprint (block 570). For each device identified in the fingerprint,the example network information processor 215 determines whether thedevice has been identified a second threshold number of times (e.g.,five times) within a second threshold period of time (e.g., the pastmonth). In the illustrated example of FIG. 5, as compared to the firstthreshold number of times and the first threshold period of time, thesecond threshold number of times and second threshold period of time areless restrictive. That is, a device that was previously seen frequentlysuch that it was added to the fingerprint, but is now seen occasionally,will remain a part of the fingerprint until it has not been seen thesecond threshold number of times within the second threshold period oftime. However, any other thresholds may additionally or alternatively beused. Devices that do not meet the second threshold number of timeswithin the second threshold period of time are removed from thefingerprint (but may later be re-added if they again become frequentlyseen). As with adding items to the fingerprint, different thresholds maybe used when considering whether to remove a wireless network or adevice from the fingerprint.

Upon addition of newly identified wireless networks and/or devices tothe fingerprint and removal of wireless networks and/or devices from thefingerprint that are no longer commonly seen, the example fingerprintreporter 250 reports the fingerprint to the fingerprint receiver 165 ofthe central facility 110 (block 590). The fingerprint receiver 165stores the reported fingerprint in the fingerprint database 170 forsubsequent analysis by the fingerprint analyzer 175. In examplesdisclosed herein, the fingerprint reporter 250 additionally transmitsthe user identifier of the panelist and/or some other information thatenables the central facility 110 to identify the panelist. In thismanner, the fingerprint may evolve over time as the habits and/or deviceownership of the panelist evolve. The example process of FIG. 5 thenrepeats to continually update the fingerprint and provide the same tothe central facility 110.

FIG. 8 is an example data table 800 representing fingerprint datacollected from fingerprint generator(s) associated with variouspanelists. The example data table 800 of FIG. 8 includes a first column810 storing a user identifier (e.g., an identifier of the panelist asreported by the fingerprint generator 115), a second column 820 storingthe reported fingerprint. In some examples, additional columnsidentifying, for example, when the most recent fingerprint was received,an IP address of the media device when the most recent fingerprint wasreceived, etc. The example data table 800 of FIG. 8 includes a firstrecord 830 and a second record 840. In practice, the example data table800 will include many more records corresponding to many morefingerprint generators 115 reporting respective fingerprints.

The first example record 830 identifies that the media device used bypanelist “A” is an Apple iPhone 7, which is commonly is nearby wirelessnetworks named “IPA”, “HOMENET”, and “STARBUCKS”, and is commonlyconnected to a same local area network as devices named “HP 6300(DESKTOP)”, “MICROSOFT SURFACE PRO”, and “APPLE IPAD.” The secondexample record 840 identifies that the media device used by panelist “B”is a Samsung Galaxy S6, which is commonly nearby wireless networks named“WIRELESSAP” and “STARBUCKS”, and is commonly connected to a same localarea network as devices named “HP PRINTER”, “GOOGLE CHROMECAST”, and“APPLE IPAD”.

FIG. 9 is a flowchart representative of example machine readableinstructions 900 which may be executed to implement the example centralfacility 110 of FIG. 1 to identify additional panelist enrollmentopportunities. The example program 900 of FIG. 9 begins when the examplefingerprint analyzer 175 parses fingerprints stored in the fingerprintdatabase 170 to determine which device categories are commonly seen(block 910). While individual fingerprints report which devices arecommonly seen, many different devices exist. Because panel participationis generally tied to whether a panelist owns a particular type of devicecapable of operating a corresponding media monitor (e.g., the mediamonitor 130), in examples disclosed herein, commonly seen devices aregrouped into categories of devices. In some examples, devices arecategorized based on an operating system of the media device. However,any other property of the device may be used for categorization such as,for example, a make and/or model of the device, a form factor of thedevice, an age of the device, a feature and/or functionality provided bythe device, etc. An example approach to parsing fingerprint data tocategorize the devices included in the fingerprint is described below inconnection with FIG. 10. A result of the parsing operation is shown inthe example data table 1100 of FIG. 11.

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 which may be executed to implement the example centralfacility of FIG. 1 to parse fingerprint data. The example process 1000of FIG. 10 begins when the example fingerprint analyzer 175 accesses areported fingerprint for a panelist (block 1005). In examples disclosedherein, each fingerprint generator reports a single fingerprint.However, in some examples, the panelist might operate multiple mediadevices that generate their own unique fingerprint. In such an example,the fingerprints may be combined and/or analyzed together.

The example fingerprint analyzer 175 extracts a primary device type fromthe fingerprint (block 1010) and stores the primary device type in aparsed fingerprint table (e.g., the parsed fingerprint data table 1100of FIG. 11) stored in the fingerprint database 170. The examplefingerprint analyzer 175 identifies a device category of interest (block1015). In the illustrated example of FIG. 10, devices are categorizedbased on an operating system of the device. However, any other propertyof the device may additionally or alternatively be used to categorize adevice. For example, a make and/or model of the device, a form factor ofthe device, an age of the device, a feature and/or functionalityprovided by the device, etc. may be used to categorize the device. Theexample fingerprint analyzer 175 determines whether the fingerprintincludes a device within the category (block 1020). In examplesdisclosed herein, the example fingerprint analyzer 175 determineswhether the fingerprint includes a device within the category byidentifying one or more patterns of device names and/or propertiesassociated with the category, and determining if any of the device namesmatch one of the one or more patterns. If the example fingerprintanalyzer 175 determines that the fingerprint does not include a devicewithin the category (block 1020 returns a result of NO), the examplefingerprint analyzer 175 stores a value indicating that no device withinthe category is present in a corresponding column of the parsedfingerprint table stored in the fingerprint database 170 (block 1030).If the example fingerprint analyzer 175 determines that the fingerprintincludes a device within the category (block 1020 returns a result ofYES), the example fingerprint analyzer 175 stores a value indicatingthat a device within the category is present in the corresponding columnof the parsed fingerprint table stored in the fingerprint database 170(block 1035). For example, the pattern “APPLE IPAD” may be used toindicate a device present in the category “APPLE DEVICE”, and uponanalyzing the fingerprint for panelist “A” (see record 830 of FIG. 8),the example fingerprint analyzer 175 identifies the device “APPLE IPAD”and records a “YES” in the corresponding column 1130 of FIG. 11.

The example fingerprint analyzer 175 determines whether there are anyadditional categories to evaluate (block 1040). If there are additionalcategories to evaluate (block 1040 returns a result of YES), the exampleprocess of blocks 1015 through 1040 is repeated until no additionalcategories exist for evaluation (until block 1040 returns a result ofNO). The example fingerprint analyzer 175 determines whether there areany additional fingerprints to evaluate (block 1050). If there areadditional fingerprints to evaluate (block 1050 returns a result ofYES), the example process of blocks 1005 through 1050 is repeated untilno additional fingerprints exist for evaluation (until block 1050returns a result of NO). FIG. 10 then terminates and control proceeds toblock 920 of FIG. 9.

FIG. 11 is an example data table 1100 representing whether thefingerprint generator(s) associated with various panelists commonly seedevices of various device categories. The example data table 1100 ofFIG. 11 may be stored in the example fingerprint database 170 of FIG. 1.The example data table 1100 includes a user identifier column 1110, aprimary device type column 1120, a first category column 1130, a secondcategory column 1140, and a third category column 1145. In theillustrated example of FIG. 11, the primary device type column 1120identifies the primary device of the corresponding panelist. In exampleswhere the panelist uses multiple media devices (each which may reporttheir own fingerprints), the primary device type column 1120 may beomitted and/or may be stored in a different location in connection withthe user identifier column 1110 (e.g., in the panelist database 160).

The example first category column 1130 identifies whether the mediadevice 112 is commonly connected to a same local area network as anApple device (e.g., an Apple™ iPad™, an Apple™ iPhone™, etc.). Theexample second category column 1140 identifies whether the media device112 is commonly connected to a same local area network as a desktopcomputer. The example third category column 1145 identifies whether themedia device 112 is commonly connected to a same local area network as aGoogle Android™ device (e.g., a Samsung™ Galaxy S6™, a Samsung™ GalaxyS7™, etc.). While in the illustrated example of FIG. 11, three categorycolumns are shown, any number of category columns having any level ofgranularity (e.g., smartphones, make of smartphone, make and model ofsmartphone, operating system of a particular make and model ofsmartphone, etc.) may additionally or alternatively be used.

The example data table 1100 of FIG. 11 includes a first record 1150 thatidentifies that panelist “A” primarily uses an Apple™ device that iscommonly connected to a same wireless network as another Apple™ deviceand a desktop computer. The example data table 1100 of FIG. 11 includesa second record 1160 that identifies that panelist “B” primarily uses anAndroid™ device that is commonly connected to a same wireless network asan Apple™ device and a desktop computer. Based on this information, apanel opportunity may exist for panelist “B”, in that they are a user ofan Android™ device but are commonly connected to a same wireless networkas an Apple™ device. From this information, it may be deduced that amember of the panelist's household (or the panelist themselves) uses anApple™ device, and might be interested in joining a panel specific touse of Apple™ devices.

Returning to FIG. 9, the example panel opportunity identifier 180accesses panel opportunity data (block 920). In examples disclosedherein, the panel opportunity data is provided by an administrator ofthe central facility 110. However, the panel opportunity data may beprovided in any other fashion. In some examples the panel opportunitydata includes parameters that indicate, for example, that users that arenot part of a particular panel but commonly see devices of a particulartype corresponding to the particular panel should be invited toparticipate in the panel corresponding to the commonly seen device type.Additionally or alternatively, the panel opportunity data may indicatethat panelists that are commonly near a particular wireless networkassociated with an establishment (e.g., a network associated with acoffee shop, an airport, a restaurant, a store, etc.) may be surveyedconcerning their patronage at the establishment.

The example panel opportunity identifier 180 queries the parsedfingerprint data (e.g., the example data table 1100 of FIG. 11, the listof wireless networks commonly nearby the media device 112) and/or otherdata associated with the panelist (e.g., the demographic data of theexample data table 300 of FIG. 3, the example panel participation datatable 400 of FIG. 4, etc.) to identify a panelist matching the panelopportunity data (block 930). In examples disclosed herein, thepanelist(s) are identified by performing a structured query languagequery against the fingerprint database 170 and/or the panelist database160. However, any other approach to identifying panelists matchingparameters defined in the panel opportunity data may additionally oralternatively be used. As an example, if the panel opportunity dataindicated that Android™ users that commonly are nearby iOS devicesshould be requested to join the iOS panel, panelist “B” may beidentified, because panelist “B” (see row 1160 of FIG. 11) primarilyuses an Android™ device and is commonly connected to a same local areanetwork as an Apple Apple™ device. However, any other parameters and/ordemographic requirement(s) may additionally or alternatively be used toidentify panelists such as, for example, the age of the panelist, thegender of the panelist, the race of the panelist, the marital status ofthe panelist, the income of the panelist and/or the household of thepanelist, the employment status of the panelist, where the panelisttypically uses the media device 112, how long the panelist has owned themedia device 112, the education level of the panelist, whether thepanelist complies with instructions from the audience measurement entityfor operating the media monitor 130, wireless networks commonly nearbythe media device of the panelist, etc.

The example panel opportunity identifier 180 transmits a message to theidentified panelist(s) (e.g., an email message, a short message service(SMS) message, etc.) (block 940). In examples disclosed herein, themessage may be message requesting the panelist to register forparticipation in another panel (e.g., a panel associated with a deviceand/or device type included in the fingerprint) (e.g., “Are you and/or afamily member interested in joining the Android™ panel?”). In examplesdisclosed herein, when joining and/or registering for another panel, thepanelist does not provide additional demographic information, as suchinformation was collected during a prior panel enrollment. However, insome examples, the panelist may be requested by the registrar 155 toconfirm their demographic information. In some examples, the message maybe a survey concerning the other devices and/or wireless networkscommonly nearby the media device 112. In some examples, the message isdisplayed by the recruitment controller 260 of the fingerprint generator115. In some examples, the message instructs the panelist to visit awebpage (e.g., a webpage identified by a URL) of the registrar 155 toenroll in an additional panel(s).

While in some examples disclosed herein, the fingerprint(s) are analyzedto identify panel opportunities (e.g., opportunities for enrollingadditional panelists), in some examples, the fingerprint(s) are analyzedto identify inconsistencies in a panelist's reported demographic data.For example, a panelist that is identified as single (e.g., livingalone), but having an associated fingerprint that identifies many otherdevices are seen during evenings and overnight, might suggest that thepanelist lives with roommates and/or a family. In such an example, thepanelist may be requested to confirm their demographic information.

FIG. 12 is a block diagram of an example processor platform 1200 capableof executing the example machine-readable instructions of FIGS. 5, 6,and/or 7 to implement the example fingerprint generator 115 of FIGS. 1and/or 2. The processor platform 1200 can be, for example, a server, apersonal computer, a mobile device (e.g., a cell phone, a smart phone, atablet such as an iPad™), a personal digital assistant (PDA), anInternet appliance, a DVD player, a CD player, a digital video recorder,a Blu-ray player, a gaming console, a personal video recorder, a set topbox, or any other type of computing device.

The processor platform 1200 of the illustrated example includes aprocessor 1212. The processor 1212 of the illustrated example ishardware. For example, the processor 1212 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1212 of the illustrated example includes a local memory1213 (e.g., a cache) and executes instructions to implement the examplenetwork scanner 210, the example network information processor 220, theexample fingerprint reporter 250, and/or the example recruitmentcontroller 260. In some examples, the processor 1212 executesinstructions to implement the media monitor 130. The processor 1212 ofthe illustrated example is in communication with a main memory includinga volatile memory 1214 and a non-volatile memory 1216 via a bus 1218.The volatile memory 1214 may be implemented by Synchronous DynamicRandom Access Memory (SDRAM), Dynamic Random Access Memory (DRAM),RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type ofrandom access memory device. The non-volatile memory 1216 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the main memory 1214, 1216 is controlled by a memorycontroller.

The processor platform 1200 of the illustrated example also includes aninterface circuit 1220. The interface circuit 1220 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1222 are connectedto the interface circuit 1220. The input device(s) 1222 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1224 are also connected to the interfacecircuit 1220 of the illustrated example. The output devices 1224 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1220 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1220 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1226 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1200 of the illustrated example also includes oneor more mass storage devices 1228 for storing software and/or data.Examples of such mass storage devices 1228 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. In the illustratedexample of FIG. 12, the example mass storage 1228 implements the exampletemporary network information data store 215 and the example fingerprintdata store 240.

The coded instructions 1232 of FIGS. 5, 6, and/or 7 may be stored in themass storage device 1228, in the volatile memory 1214, in thenon-volatile memory 1216, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

FIG. 13 is a block diagram of an example processor platform capable ofexecuting the example machine-readable instructions of FIGS. 9 and/or 10to implement the example central facility 110 of FIG. 1. The processorplatform 1300 can be, for example, a server, a personal computer, anInternet appliance, or any other type of computing device.

The processor platform 1300 of the illustrated example includes aprocessor 1312. The processor 1212 of the illustrated example ishardware. For example, the processor 1312 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1312 of the illustrated example includes a local memory1313 (e.g., a cache) and executes instructions to implement the exampleregistrar 155, the example fingerprint receiver 165, the examplefingerprint analyzer 175, and/or the example panel opportunityidentifier 180. The processor 1312 of the illustrated example is incommunication with a main memory including a volatile memory 1314 and anon-volatile memory 1316 via a bus 1318. The volatile memory 1314 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 1316 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 1314, 1316 iscontrolled by a memory controller.

The processor platform 1300 of the illustrated example also includes aninterface circuit 1320. The interface circuit 1320 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1322 are connectedto the interface circuit 1320. The input device(s) 1322 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1324 are also connected to the interfacecircuit 1320 of the illustrated example. The output devices 1324 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1320 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1320 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1326 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1300 of the illustrated example also includes oneor more mass storage devices 1328 for storing software and/or data.Examples of such mass storage devices 1328 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. In the illustratedexample of FIG. 13, the example mass storage 1328 implements the examplepanelist database 160 and the example fingerprint database 170.

The coded instructions 1332 of FIGS. 9 and/or 10 may be stored in themass storage device 1328, in the volatile memory 1314, in thenon-volatile memory 1316, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus and articles of manufacture enable panelists to berecruited for participation in panels that are specific to the type(s)of device that they frequently use (e.g., an Apple iPhone™, a desktopcomputer, a Google Android™ tablet, etc.). By generating a fingerprintincluding information identifying devices that are commonly connected toa same local area network as a media device operated by a panelist,opportunities to recruit existing panelists for participation inadditional panels can be easily identified. Moreover, because generationof such fingerprint is performed by a fingerprint generator at the mediadevice of the panelist, changes in the devices that are commonlyconnected to the same local area network as the media device can bedetected, and such changes can be acted upon. For example, whereas inprior systems all panelists might be requested to join additional panelsperiodically (e.g., every month, every quarter year, etc.), in examplesdisclosed herein, targeted panelists can be prompted to join anadditional panel soon after acquisition of a new device. Proactivelyprompting a panelist to join an additional panel enables a more holisticmeasurement to be made of the media presented to the panelist (e.g., asno or few media devices are omitted from the monitoring effort). Inaddition to panel recruitment messages, example approaches disclosedherein enable other messages (e.g., surveys, questionnaires, etc.) to bepresented to panelists based on devices and/or wireless networks thatthe media device is commonly near.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus for expanding panelist enrollment,the apparatus comprising: a network interface to access monitoring datafrom a media device, the monitoring data to identify media that waspresented at least one of by the media device or within a physicalproximity of the media device; a registrar to receive registrationinformation from a panelist, the registration information includingdemographic information associated with the panelist and informationabout the media device associated with the panelist; a panelist databaseto store the demographic information associated with the panelist, thepanelist database to store a record associating an enrollment of thepanelist in a first panel corresponding to a first category of the mediadevice; a fingerprint receiver to access a fingerprint received from themedia device associated with the panelist, the fingerprint identifyingdevices communicating on a same local area network as the media device,the same local area network identified based on a subnet that a privateInternet protocol address of the media device is part of to reduce asearch space of potential devices; a fingerprint analyzer to determinewhether a second device within a second category of devicescorresponding to a second panel is identified in the fingerprint, thesecond category of devices not including the media device; and a panelopportunity identifier to determine that the panelist meets ademographic requirement for participation in the second panel, the panelopportunity identifier to prompt the panelist to join the second panelin response to the determination that the second device within thesecond category of devices is identified in the fingerprint and that thepanelist meets a demographic requirement for participation in the secondpanel, the prompting of the panelist to be performed without requestingadditional demographic information from the panelist.
 2. The apparatusof claim 1, wherein the fingerprint identifies wireless local areanetworks within a wireless signal communication range of the mediadevice.
 3. The apparatus of claim 1, wherein the panel opportunityidentifier is to prompt the panelist to join the second panel inresponse to a determination that the panelist is not enrolled in thesecond panel.
 4. The apparatus of claim 1, wherein the registrar is toregister the panelist for the second panel, wherein the panelist doesnot provide demographic information when joining the second panel. 5.The apparatus of claim 1, wherein the fingerprint analyzer is toevaluate a pattern representative of a device identifier associated withone or more devices in the second category of devices to determinewhether the second device within the second category of devicescorresponding to the second panel is identified in the fingerprint. 6.The apparatus of claim 1, wherein the first panel corresponds to devicesoperating a first operating system and the second panel corresponds todevices operating a second operating system different from the firstoperating system.
 7. The apparatus of claim 1, wherein the panelopportunity identifier is to prompt the panelist to join the secondpanel by transmitting a message to the media device.
 8. A method toexpand panelist enrollment, the method comprising: accessingregistration information from a panelist, the registration informationincluding demographic information associated with the panelist andinformation about a media device associated with the panelist; storingthe demographic information associated with the panelist and a recordassociating an enrollment of the panelist in a first panel correspondingto a first category of the media device; accessing a fingerprintreceived from the media device associated with the panelist, thefingerprint identifying devices communicating on a same local areanetwork as the media device, the same local area network identifiedbased on a subnet that a private Internet protocol address of the mediadevice is part of to reduce a search space of potential devices;parsing, by executing an instruction with a processor, the fingerprintto determine whether a second device within a second category of devicescorresponding to a second panel is identified in the fingerprint, thesecond category of devices not including the media device; determiningthat the panelist meets a demographic requirement for participation inthe second panel without requesting additional demographic informationfrom the panelist; and in response to the determination that the seconddevice within the second category of devices is identified in thefingerprint and that the panelist meets the demographic requirement forparticipation in the second panel, prompting the panelist to join thesecond panel, the prompting of the panelist to be performed withoutrequesting additional demographic information from the panelist.
 9. Themethod of claim 8, wherein the fingerprint identifies wireless localarea networks nearby the media device.
 10. The method of claim 8,wherein the prompting of the panelist to join the second panel occurs inresponse to a determination that the panelist is not enrolled in thesecond panel.
 11. The method of claim 8, wherein the panelist does notprovide demographic information when joining the second panel.
 12. Themethod of claim 8, wherein the parsing of the fingerprint includesevaluating a pattern representative of a device identifier associatedwith one or more devices in the second category of devices.
 13. Themethod of claim 8, wherein the first panel corresponds to devicesoperating a first operating system and the second panel corresponds todevices operating a second operating system different from the firstoperating system.
 14. The method of claim 8, wherein the prompting ofthe panelist to join the second panel includes transmitting a message tothe media device.
 15. A non-transitory machine-readable mediumcomprising instructions which, when executed, cause a machine to atleast: access registration information from a panelist, the registrationinformation including demographic information associated with thepanelist and information about a media device associated with thepanelist; store the demographic information associated with the panelistand a record associating an enrollment of the panelist in a first panelcorresponding to a first category of the media device; access afingerprint received from the media device associated with the panelist,the fingerprint identifying devices communicating on a same local areanetwork as the media device, the same local area network identifiedbased on a subnet that a private Internet protocol address of the mediadevice is part of to reduce a search space of potential devices; parsethe fingerprint to determine whether a second device within a secondcategory of devices corresponding to a second panel is identified in thefingerprint, the second category of devices not including the mediadevice; determine that the panelist meets a demographic requirement forparticipation in the second panel without requesting additionaldemographic information from the panelist; and in response to thedetermination that the second device within the second category ofdevices is identified in the fingerprint and that the panelist meets ademographic requirement for participation in the second panel, promptthe panelist to join the second panel, the prompting of the panelist tobe performed without requesting additional demographic information fromthe panelist.
 16. The non-transitory machine-readable medium of claim15, wherein the prompting of the panelist to join the second paneloccurs in response to a determination that the panelist is not enrolledin the second panel.
 17. The non-transitory machine-readable medium ofclaim 15, further including instructions which, when executed, cause themachine to evaluate a pattern representative of a device identifierassociated with one or more devices in the second category of devices.